1. Semantic integration of data in database systems and ontologies
Technical university of Liberec
Faculty of mechatronics
Semantic integration of data in database systems and ontologies
Ing. Petra Šeflová

2. Integration of data
- merging a set given schemas into global schema
Semantic integration
- part of concept integration of data
- be focusing on data exchange between applications in the light of their meaning, content and required business rules

3. Integration of data wrapper
Example
homeseekers.com
Source schema
wrapper
mediated schema
Find houses with four bathrooms and price under $
realestate.com
greathomes.com
A data integration system in the real estate domain.

4. Applications Catalog integration in B2B applications E-commerce
Bioinformatics
P2P Databases
Agent communications
Web services Integration

5. Key commonalities application of Semantic integration
Use structured representation
(e.g. relational schemas and XML DTDs)
Must resolve heterogenities with respect to the schema and their data
Enable their manipulation
Merging the schemas
Computing differences
Enable translation of data and queries across the schemas/ontologies

6. Database schema Ontology
Present definition physical system layout (database)
Ontology
System of knowledge about world
Claimless on coherence (lot of partial ontology)
Frequently specific created artefact
Definition of Gruber: Ontology is formal, explicit specification sharing conceptualization.

7. Problems of Semantic integration
Semantic of elements can be inferred from only a few information sources
Creators of data
Dokumentation
Associated schema and data
Schema element are typically matched based on clues in the schema and data
Schema and data clues are often incomlpete
Matching is often subjective, depending in the application

8. Matching process
Take as input two schemas/ontologies, each consisting of a set discrete entities, and determine as output the relationships holding between these entities

9. Schema S Houses Schema T Agents Location Price ($) Agent-id
Atlanta, GA
360,000 32
Raleigh, NC
430,000 15
Schema T
Area
list-price
Agent-address
Agent-name
Denver,CO
550,000
Boulder,CO
Laura Smith
Atlanta, GA
370,800
Athens,
Mike Brown
Agents Id
Name
city
state 32
Mike Brown
Athlanta GA 15
Jean Laup
Relaign NC
Example : The schema of two relational database S and T on house
listing, and the semantic correspondence between them

10. Matching techniques
Two groups
Rule-based
Learning-based

11. Rule-based solutions
Many of the early as well as current matching solutions employ hand-crafted rules
Exploit schema information
Element names
Data types
Structures
Integrity constraints
Can provide a quick and concise method to capture valuable user knowledge about domain

12. Rule-based solutions Benefits Drawback For example :
„relatively inexpensive“
Do not require training
Operate only on schema
Drawback
They cannot exploit data instance effectively
They cannot exploit previous matching efforts
For example :
TranScm
DIKE
MOMIS
CUPID

13. TranScm DIKE MOMIS CUPID Employs rules such as
„two elements match if they have the same name (allowing synonyms) and the same number of subelements
DIKE
Computes similarity between two schema element based on similarity of the characteristics of the element and similarity of related elements
MOMIS
Compute similarity of schema elements as a weighted suma of the similarity of name,data type and substructure
CUPID
Employs rules that categorize elements based on names, data types and domains

14. Learning-based solutions
Exploit both schema and data information
They do exploit previous matching efforts
Examples:
SemInt system
LSD system
iMAP system
Autocomplex
Automatch

15. Autoplex and Automatch
SemInt
Uses a neuralnetwork learning approaches
It matched schema elements based on attribute specifications and statistic of data content
LSD
Employs Naive Bayes over data instance
Develop novel learning solution exploit the hierarchical nature of XML data
iMAP
Matches the schemas of two sources by analyzing the description of objects that are found in both sources
Autoplex and Automatch
Use a Naive Bayes learning approach that exploits data instances to match element

16. The Matching dimensions
Input dimension
Process dimension
Output dimensions

17. Input dimension Concern the kind of input on which algorithm operate
First dimension
Algorithms depending on the data/ conceptual model in which ontologies or schemas are expressed
Second dimension
Depend on the kind of data algorithms exploit
Different approaches exploit different information of the input data/conceptual models
Schema-level information
Instance data
Exploit both

18. Process dimensions
Classification of the matching process could be based on its general properties
It depends on the approximate or exact nature of its computation
Exact algorithms compute the absolute solution to a problem
Approximate algorithms sacrifice exactness to performance
Three large classes based on intrinsic input, external resources or some semantic theory
Syntactic
External
Semantic

19. Output dimensions Concern the form of the result they produce
One-to-one correspondence
Is any relation suitable
Has it to be final mapping element
System deliver a graded answer
Correspondences hold with 98% confidence
Correspondences hold with 4/5 probability
All-or-nothing answer
Correspondences using distance measuring
Kind of relations between entities a system can provide
Equivalence
Subsumption
Incompatibility

20. Classification of elementary schema-based matching approaches
Schema-Based Matching Techniques
Element-level
Structure-level
Syntantic
Syntactic
External
Linguistic
Internal
Relational
Semantic
Structural
Terminological
String-
Based
Language-
Resource
Contraint-
Upper
Level
Formal
ontologies
Graph-
Taxonomy-
Repository of
Structure
Model-
Alignment
reuse
Basic
Techniques
layer
Granuality/Input Interpretation layer

21. Element-level vs structure-level
Element-level matching techniques compute mapping elements by analyzing entities in isolation
Ignoring their relation with other entities
Structure-level techniques compute mapping elements by analyzing how entities appear together in a structure

22. Internal vs external techniques
Interal
Exploiting information which comes only with input schema/ontologies
Syntactic interpretation of input
Sematic interpretation of input
External
Exploit auxiliary (external) resources of domain to interpret the input
Resources :
Human input
Some thesaurus expressing the relationship between terms

23. Schema Matching vs Ontology Matching Differences
Database schema often do not provide explicit semantics for their data
Semantics is usually specified explicitly at design-time
Usually performed with the help of techniques trying to guess the meaning encoded in the schemas
Ontologies are logical systems that themselves obey some formal semantics
Primarily try to exploit knowledge explicitly encoded in the ontologies

24. Schema Matchin vs Ontology Matching Commonalities
Ontologies and schemas are similar in the sense :
Provide a vocablurary of terms that describes a domain of interest
Constrain the meaning of terms used in vocablurary
Schema and ontologies are found in such enviroment as the Semantic web

25. Sources :
Natalya F.Noy : Semantic Integration: A survey of Ontology-Based Approaches
AnHai Doan, Alon Y. Haley: Semantic Integration in the Database Community: A Brief Survey
P.Schvaiko, J. Euzenat: A Survey of schema-based Matching Approaches
G. Antonious, F. van Harmelen: A Semantic Web Primer
R. Araújo, H. Sofia Pinto: Toward Semantics-based ontology similarity
H. Wache, T. Vögele, U. Visser, H. Stuckenschmidt, G. Shuster, H. Neumann and S. Húbner: Ontology-based integration of information – A survey existing Approaches
E. Rahm, P.A. Bernstein: A survey of approaches to automatic schema matching